Fast neural learning and control of discrete-time nonlinear systems
- 1 March 1995
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Systems, Man, and Cybernetics
- Vol. 25 (3) , 478-488
- https://doi.org/10.1109/21.364860
Abstract
The problem of learning control for a general class of discrete-time nonlinear systems is addressed in this paper using multilayered neural networks (MNNs) with feedforward connections. A suitable extension of the concept of input-output linearization of discrete-time nonlinear systems is used to develop the control schemes for both output tracking and model reference control purposes. The ability of MNNs to model arbitrary nonlinear functions is incorporated to approximate the unknown nonlinear input-output relationship and its inverse using a new weight learning algorithm. In order to overcome the difficulties associated with simultaneous online identification and control in neural networks based adaptive control systems, the new learning control architectures are developed for both adaptive tracking and adaptive model reference control systems with online identification and control ability. The potentials of the proposed methods are demonstrated by simulation examplesKeywords
This publication has 23 references indexed in Scilit:
- Decoupled extended Kalman filter training of feedforward layered networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Neural networks for control systems—A surveyAutomatica, 1992
- Properties of neural networks with applications to modelling non-linear dynamical systemsInternational Journal of Control, 1992
- Gradient methods for the optimization of dynamical systems containing neural networksIEEE Transactions on Neural Networks, 1991
- Neural networks for nonlinear internal model controlIEE Proceedings D Control Theory and Applications, 1991
- Back-propagation neural networks for nonlinear self-tuning adaptive controlIEEE Control Systems Magazine, 1990
- Identification and control of dynamical systems using neural networksIEEE Transactions on Neural Networks, 1990
- Nonlinear Dynamical Control SystemsPublished by Springer Nature ,1990
- Parallel recursive prediction error algorithm for training layered neural networksInternational Journal of Control, 1990
- A multilayered neural network controllerIEEE Control Systems Magazine, 1988